ACBM: An Integrated Agent and Constraint Based Modeling Framework for Simulation of Microbial Communities.

Area of Science:

• Microbial Ecology
• Computational Biology
• Systems Biology

Background:

• Realistic modeling of microbial communities requires quantitative comparison with experimental data.
• Existing models often lack the ability to capture complex spatial and temporal dynamics or intracellular constraints.

Purpose of the Study:

• To introduce a novel integrated agent and constraint based modeling (ACBM) framework.
• To enhance the predictive accuracy of microbial community simulations.

Main Methods:

• Developed an integrated agent-based and constraint-based modeling framework (ACBM).
• Modeled cell populations in three-dimensional space to predict spatial and temporal dynamics and metabolic interactions.
• Integrated transcriptomic data with metabolic models to incorporate intracellular constraints.

Main Results:

• ACBM improved predictions for batch growth of C. beijerinckii and two-species communities compared to previous models.
• ACBM accurately predicted growth rate, biomass, glucose, and acidic product concentrations over time for E. coli.
• The framework successfully predicted metabolic shifts between log and stationary phases and estimated starved cells under heterogeneous feeding.

Conclusions:

• The ACBM framework provides a more realistic and quantitatively comparable approach to modeling microbial communities.
• ACBM accurately captures spatial, temporal, and metabolic dynamics, including intracellular constraints.
• A percentage of cells are consistently subject to starvation in high-volume bioreactors.